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Power your journey to AI
The IBM® InfoSphere® DataStage® offering is an industry-leading cloud-ready data integration solution. It provides real-time delivery of trusted data into data lakes, data warehouses, or any other multi or hybrid cloud environment to feed business-ready data into AI applications. Real-time analytics is now easier than ever, with a cloud-native architecture built on containers and microservices.
IBM DataStage on IBM Cloud Pak® for Data allows you to simplify operations by automating and accelerating administration tasks to reduce total cost of ownership (TCO) and meet business service level agreements (SLAs). Avoid vendor lock-in by deploying on any cloud or multi-cloud environment, and leverage industry leading security, reliability, and scalability from Red Hat® OpenShift®.
Accelerate DataOps and AI innovation through automated integration templates and seamless out-of-the-box integration with governance, BI, data virtualization and data science services with IBM DataStage on IBM Cloud Pak for Data.
Design once, run anywhere with built in workload balancing, parallelism and dynamic scalability
Separate the design from the runtime to run remote jobs where your data resides. A parallel engine optimizes ETL performance and automatic load balancing maximizes throughput while scaling with your data volumes.
Automated delivery pipelines to release jobs to production
Container-based integration components along with git-based source control tooling allow for automation of CI/CD pipelines for jobs from dev to test to production.
User-friendly design with infused ML capabilities and rich set of connectors and transformations
IBM DataStage Flow designer, with infused machine learning capabilities, built-in search, and prebuilt connectors and transformations, allows you to quickly create and run DataStage jobs and connect with governance.
In-flight data quality and data security for trusted data delivery
Automatically resolve quality issues using IBM InfoSphere QualityStage® when data is ingested by target environments such as data lakes. Provide metadata support for policy-driven access to sensitive data.